Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
12 tokens/sec
GPT-4o
12 tokens/sec
Gemini 2.5 Pro Pro
41 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
37 tokens/sec
DeepSeek R1 via Azure Pro
33 tokens/sec
2000 character limit reached

From Data to Insights: A Comprehensive Survey on Advanced Applications in Thyroid Cancer Research (2401.03722v1)

Published 8 Jan 2024 in cs.LG

Abstract: Thyroid cancer, the most prevalent endocrine cancer, has gained significant global attention due to its impact on public health. Extensive research efforts have been dedicated to leveraging AI methods for the early detection of this disease, aiming to reduce its morbidity rates. However, a comprehensive understanding of the structured organization of research applications in this particular field remains elusive. To address this knowledge gap, we conducted a systematic review and developed a comprehensive taxonomy of machine learning-based applications in thyroid cancer pathogenesis, diagnosis, and prognosis. Our primary objective was to facilitate the research community's ability to stay abreast of technological advancements and potentially lead the emerging trends in this field. This survey presents a coherent literature review framework for interpreting the advanced techniques used in thyroid cancer research. A total of 758 related studies were identified and scrutinized. To the best of our knowledge, this is the first review that provides an in-depth analysis of the various aspects of AI applications employed in the context of thyroid cancer. Furthermore, we highlight key challenges encountered in this domain and propose future research opportunities for those interested in studying the latest trends or exploring less-investigated aspects of thyroid cancer research. By presenting this comprehensive review and taxonomy, we contribute to the existing knowledge in the field, while providing valuable insights for researchers, clinicians, and stakeholders in advancing the understanding and management of this disease.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (229)
  1. F. M. Salman, Abu-Naser, and S. S., “Thyroid knowledge based system,” International Journal of Academic Engineering Research, vol. 3, no. 5, pp. 11–20, 2019.
  2. Z. W. Baloch, S. L. Asa, J. A. Barletta, R. A. Ghossein, C. C. Juhlin, C. K. Jung, V. A. LiVolsi, M. G. Papotti, M. Sobrinho-Simões, G. Tallini, and O. Mete, “Overview of the 2022202220222022 who classification of thyroid neoplasms,” Endocrine Pathology, vol. 33, p. 27–63, 2022.
  3. U. R. Acharya, G. Swapna, S. V. Sree, F. Molinari, S. Gupta, R. H. Bardales, A. Witkowska, and J. S. Suri, “A review on ultrasound-based thyroid cancer tissue characterization and automated classification,” Technology in Cancer Research & Treatment, vol. 13, no. 4, pp. 289–301, 2014. PMID: 24206204.
  4. P. Nix, A. Nicolaides, and A. P. Coatesworth, “Thyroid cancer review 1: presentation and investigation of thyroid cancer,” International journal of clinical practice, vol. 59, no. 11, pp. 1340–1344, 2005.
  5. C. L. Vecchia, M. Malvezzi, C. Bosetti, W. Garavello, P. Bertuccio, F. Levi, and E. Negri, “Thyroid cancer mortality and incidence: a global overview,” International journal of cancer, vol. 136, no. 9, pp. 2187–2195, 2015.
  6. A. C. Society, “Cancer facts & figures 2022,” 2022.
  7. M. Pizzato, M. Li, J. Vignat, M. Laversanne, D. Singh, C. La Vecchia, and S. Vaccarella, “The epidemiological landscape of thyroid cancer worldwide: Globocan estimates for incidence and mortality rates in 2020202020202020,” The Lancet Diabetes & Endocrinology, vol. 10, no. 4, pp. 264–272, 2022.
  8. R.-M. Feng, Y.-N. Zong, S.-M. Cao, and R.-H. Xu, “Current cancer situation in china: good or bad news from the 2018 global cancer statistics?,” Cancer communications, vol. 39, no. 1, pp. 1–12, 2019.
  9. M. Massimino, D. B. Evans, M. Podda, C. Spinelli, P. Collini, N. Pizzi, and A. Bleyer, “Thyroid cancer in adolescents and young adults,” Pediatric Blood & Cancer, vol. 65, no. 8, 2018.
  10. M. J. Kim, D. Kim, J. S. Koo, J. H. Lee, and K.-H. Nam, “Vitamin d receptor expression and its clinical significance in papillary thyroid cancer,” Technology in Cancer Research & Treatment, vol. 21, p. 15330338221089933, 2022.
  11. K. Suzuki, V. Saenko, S. Yamashita, and N. Mitsutake, “Radiation-induced thyroid cancers: Overview of molecular signatures,” Cancers, vol. 11, no. 9, 2019.
  12. S. Buscemi, F. M. Massenti, S. Vasto, F. Galvano, C. Buscemi, D. Corleo, A. M. Barile, G. Rosafio, N. Rini, and C. Giordano, “Association of obesity and diabetes with thyroid nodules,” Endocrine, vol. 60, no. 2, pp. 339–347, 2018.
  13. J. Ma, M. Huang, L. Wang, W. Ye, Y. Tong, and H. Wang, “Obesity and risk of thyroid cancer: evidence from a meta-analysis of 21 observational studies,” Medical science monitor: international medical journal of experimental and clinical research, vol. 21, p. 283, 2015.
  14. H. Zhao, H. Li, and T. Huang, “High iodine intake and central lymph node metastasis risk of papillary thyroid cancer,” Journal of Trace Elements in Medicine and Biology, vol. 53, pp. 16–21, 2019.
  15. K. Kim, S. W. Cho, Y. J. Park, K. E. Lee, D.-W. Lee, and S. K. Park, “Association between iodine intake, thyroid function, and papillary thyroid cancer: A case-control study,” Endocrinology and metabolism, vol. 36, no. 4, 2021.
  16. A. Cho, Y. Chang, J. Ahn, H. Shin, and S. Ryu, “Cigarette smoking and thyroid cancer risk: a cohort study,” British journal of cancer, vol. 119, no. 5, 2018.
  17. J. S. D. Kust and N. Mateša, “Bethesda thyroid categories and family history of thyroid disease,” Clinical endocrinology, vol. 88, no. 3, pp. 468–472, 2018.
  18. G. Kline and H. Sadrzadeh, “Chapter 2222 - thyroid disorders,” in Endocrine Biomarkers (H. Sadrzadeh and G. Kline, eds.), pp. 41–93, Elsevier, 2017.
  19. R. G, B. C, R. B, R. A, and M. Bienvenu-Perrard, “The thyroid imaging reporting and data system (tirads) for ultrasound of the thyroid,” Journal de radiologie, vol. 92, pp. 701–713, 2011.
  20. A. T. Association, “Thyroid imaging reporting and data systems (tirads) accurately determine the risk of cancer in small thyroid nodules,” Clinical Thyroidology for the Public, vol. 11, pp. 7–8, 2018.
  21. R. Stewart, Y. J. Leang, C. R. Bhatt, S. Grodski, J. Serpell, and J. C.Lee, “Quantifying the differences in surgical management of patients with definitive and indeterminate thyroid nodule cytology,” European Journal of Surgical Oncology, vol. 46, pp. 252–257, 2020.
  22. Z. W. Baloch, S. Fleisher, V. A. LiVolsi, and P. K. Gupta, “Diagnosis of “follicular neoplasm”: a gray zone in thyroid fine-needle aspiration cytology,” Diagnostic cytopathology, vol. 26, no. 1, pp. 41–44, 2002.
  23. F. sheng Ouyang, B. liang Guo, L. zhu Ouyang, Z. wei Liu, S. jia Lin, W. Meng, X. yi Huang, H. xiong Chen, Q. gen Hu, and S. ming Yang, “Comparison between linear and nonlinear machine-learning algorithms for the classification of thyroid nodules,” European Journal of Radiology, vol. 113, pp. 251–257, 2019.
  24. D. S. Dean and H. Gharib, “Fine-needle aspiration biopsy of the thyroid gland,” Endotext [Internet], 2015.
  25. J. P. Walsh, “Managing thyroid disease in general practice,” Medical Journal of Australia, vol. 205, no. 4, pp. 179–184, 2016.
  26. S. Jegerlehner, J.-L. Bulliard, D. Aujesky, N. Rodondi, S. Germann, I. Konzelmann, A. Chiolero, and N. W. Group, “Overdiagnosis and overtreatment of thyroid cancer: A population-based temporal trend study,” PloS one, vol. 12, no. 6, 2017.
  27. E. K. Alexander and P. R. Larsen, “High dose 131i therapy for the treatment of hyperthyroidism caused by graves’ disease,” The Journal of Clinical Endocrinology & Metabolism, vol. 87, pp. 1073–1077, 03 2002.
  28. B. Schmidbauer, K. Menhart, D. Hellwig, and J. Grosse, “Differentiated thyroid cancer—treatment: State of the art,” International Journal of Molecular Sciences, vol. 18, no. 6, 2017.
  29. H. N. Lee, J. Y. An, K. M. Lee, E. J. Kim, W. S. Choi, and D. Y. Kim, “Salivary gland dysfunction after radioactive iodine (i-131) therapy in patients following total thyroidectomy: emphasis on radioactive iodine therapy dose,” Clinical Imaging, vol. 39, no. 3, pp. 396–400, 2015.
  30. M. Melissa, C.S., “Thyroid cancer symptoms, signs, treatment, types & survival rate,” 2020.
  31. E. L. Mazzaferri and S. M. Jhiang, “Long-term impact of initial surgical and medical therapy on papillary and follicular thyroid cancer,” The American Journal of Medicine, vol. 97, no. 5, pp. 418–428, 1994.
  32. W. Simpson, S. McKinney, J. Carruthers, M. Gospodarowicz, S. Sutcliffe, and T. Panzarella, “Papillary and follicular thyroid cancer: Prognostic factors in 1,578 patients,” The American Journal of Medicine, vol. 83, no. 3, pp. 479–488, 1987.
  33. F. Borson-Chazot, S. Causeret, J.-C. Lifante, M. Augros, N. Berger, and J.-L. Peix, “Predictive factors for recurrence from a series of 74 children and adolescents with differentiated thyroid cancer,” World journal of surgery, vol. 28, no. 11, pp. 1088–1092, 2004.
  34. M. R. Pelizzo, I. M. Boschin, A. Toniato, A. Piotto, C. Pagetta, M. D. Gross, A. F. Al-Nahhas, and D. Rubello, “Papillary thyroid carcinoma: 35-year outcome and prognostic factors in 1858 patients,” Clinical nuclear medicine, vol. 32, no. 6, pp. 440–444, 2007.
  35. R. H. Grogan, S. P. Kaplan, H. Cao, R. E. Weiss, L. J. DeGroot, C. A. Simon, O. M. Embia, P. Angelos, E. L. Kaplan, and R. B. Schechter, “A study of recurrence and death from papillary thyroid cancer with 27 years of median follow-up,” Surgery, vol. 154, no. 6, pp. 1436–1447, 2013.
  36. S. jin Kim, S. Y. Park, Y. J. Lee, E. K. Lee, S. ki Kim, T. H. Kim, Y.-S. Jung, J. Ryu, J. P. Myong, and K.-W. Chung, “Risk factors for recurrence after therapeutic lateral neck dissection for primary papillary thyroid cancer,” Annals of surgical oncology, vol. 21, no. 6, pp. 1884–1890, 2014.
  37. M. Mourad, S. Moubayed, A. Dezube, Y. Mourad, K. Park, A. Torreblanca-Zanca, J. S. Torrecilla, J. C. Cancilla, and J. Wang, “Machine learning and feature selection applied to seer data to reliably assess thyroid cancer prognosis,” Scientific Reports, vol. 10, no. 1, pp. 1–11, 2020.
  38. T. S. Wang, K. Cheung, F. Farrokhyar, S. A. Roman, and J. A. Sosa, “A meta-analysis of the effect of prophylactic central compartment neck dissection on locoregional recurrence rates in patients with papillary thyroid cancer,” Annals of surgical oncology, vol. 20, no. 11, pp. 3477–3483, 2013.
  39. T. Zetoune, X. Keutgen, D. Buitrago, H. Aldailami, H. Shao, M. Mazumdar, T. J. F. III, and R. Zarnegar, “Prophylactic central neck dissection and local recurrence in papillary thyroid cancer: a meta-analysis,” Annals of surgical oncology, vol. 17, no. 12, pp. 3287–3293, 2010.
  40. D. Van Nostrand, “The benefits and risks of i-131 therapy in patients with well-differentiated thyroid cancer,” Thyroid, vol. 19, no. 12, pp. 1381–1391, 2009.
  41. I. Kononenko, “Machine learning for medical diagnosis: history, state of the art and perspective,” Artificial Intelligence in Medicine, vol. 23, no. 1, pp. 89–109, 2001.
  42. A. Bikas and K. D. Burman, “Epidemiology of thyroid cancer,” The thyroid and its diseases, pp. 541–547, 2019.
  43. K. Hińcza, A. Kowalik, and A. Kowalska, “Current knowledge of germline genetic risk factors for the development of non-medullary thyroid cancer,” Genes, vol. 10, no. 7, 2019.
  44. Y. Yeo, S.-H. Ma, Y. Hwang, P. L. Horn-Ross, A. Hsing, K.-E. Lee, Y. J. Park, D.-J. Park, K.-Y. Yoo, and S. K. Park, “Diabetes mellitus and risk of thyroid cancer: A meta-analysis,” PLOS ONE, vol. 9, pp. 1–11, 06 2014.
  45. D. Gąsior-Perczak, I. Pałyga, M. Szymonek, A. Kowalik, A. Walczyk, J. Kopczyński, K. Lizis-Kolus, T. Trybek, E. Mikina, D. Szyska-Skrobot, K. Gadawska-Juszczyk, S. Hurej, A. Szczodry, A. Słuszniak, J. Słuszniak, R. Mężyk, S. Góźdź, and A. Kowalska, “The impact of bmi on clinical progress, response to treatment, and disease course in patients with differentiated thyroid cancer,” PLOS ONE, vol. 13, pp. 1–18, 10 2018.
  46. L. Barrea, M. Gallo, R. M. Ruggeri, P. D. Giacinto, F. Sesti, N. Prinzi, V. Adinolfi, V. Barucca, V. Renzelli, G. Muscogiuri, A. Colao, R. Baldelli, and on behalf of E.O.L.O. Group, “Nutritional status and follicular-derived thyroid cancer: An update,” Critical Reviews in Food Science and Nutrition, vol. 61, no. 1, pp. 25–59, 2021.
  47. L. Huang, X. Feng, W. Yang, X. Li, K. Zhang, S. Feng, F. Wang, and X. Yang, “Appraising the effect of potential risk factors on thyroid cancer: A mendelian randomization study,” The Journal of Clinical Endocrinology & Metabolism, 04 2022.
  48. M. Kaushik, R. Sharma, S. A. Peious, M. Shahin, S. B. Yahia, and D. Draheim, “A systematic assessment of numerical association rule mining methods,” SN Computer Science, vol. 2, no. 5, pp. 1–13, 2021.
  49. C.-H. Wang, T.-Y. Lee, K.-C. Hui, and M.-H. Chung, “Mental disorders and medical comorbidities: Association rule mining approach,” Perspectives in Psychiatric Care, vol. 55, no. 3, pp. 517–526, 2019.
  50. S. Cha and S.-S. Kim, “Comorbidity patterns of mood disorders in adult inpatients: Applying association rule mining,” Healthcare, vol. 9, no. 9, 2021.
  51. M. Tandan, Y. Acharya, S. Pokharel, and M. Timilsina, “Discovering symptom patterns of covid-19 patients using association rule mining,” Computers in Biology and Medicine, vol. 131, p. 104249, 2021.
  52. S. J. Lee and K. B. Cartmell, “An association rule mining analysis of lifestyle behavioral risk factors in cancer survivors with high cardiovascular disease risk,” Journal of Personalized Medicine, vol. 11, no. 5, 2021.
  53. A. L. Buczak and C. M. Gifford, “Fuzzy association rule mining for community crime pattern discovery,” in ACM SIGKDD Workshop on Intelligence and Security Informatics, ISI-KDD ’10, Association for Computing Machinery, 2010.
  54. D. Li, D. Yang, and J. Zhang, “Arb: Knowledge discovery and disease diagnosis on thyroid disease diagnosis integrating association rule with bagging algorithm,” Engineering Letters, vol. 28, no. 2, 2020.
  55. D. Li, D. Yang, J. Zhang, and X. Zhang, “Ar-ann: Incorporating association rule mining in artificial neural network for thyroid disease knowledge discovery and diagnosis,” IAENG International Journal of Computer Science, vol. 47, no. 1, pp. 25–36, 2020.
  56. R. Agrawal and R. Srikant, “Fast algorithms for mining association rules,” in Proc. 20th int. conf. very large data bases (A. K. Laha, ed.), vol. 1215, pp. 487–499, VLDB, 1994.
  57. D. Taniar, W. Rahayu, V. Lee, and O. Daly, “Exception rules in association rule mining,” Applied Mathematics and Computation, vol. 205, no. 2, pp. 735–750, 2008. Special Issue on Advanced Intelligent Computing Theory and Methodology in Applied Mathematics and Computation.
  58. T. Scheffer, “Finding association rules that trade support optimally against confidence,” in 5th European Conference on Principles of Data Mining and Knowledge Discovery, pp. 424–435, Springer, 2001.
  59. M. Zhai, D. Zhang, J. Long, Y. Gong, F. Ye, S. Liu, and Y. Li, “The global burden of thyroid cancer and its attributable risk factor in 195 countries and territories: A systematic analysis for the global burden of disease study,” Cancer Medicine, vol. 10, no. 13, pp. 4542–4554, 2021.
  60. M. Mileva1, B. Stoilovska, A. Jovanovska, A. Ugrinska, G. Petrushevska, S. Kostadinova-Kunovska, D. Miladinova, and V. Majstorov, “Thyroid cancer detection rate and associated risk factors in patients with thyroid nodules classified as bethesda category iii,” Radiology and oncology, vol. 52, no. 4, p. 370, 2018.
  61. G. Azizi and C. D. Malchoff, “Autoimmune thyroid disease: A risk factor for thyroid cancer,” Endocrine Practice, vol. 17, no. 2, pp. 201–209, 2011.
  62. A. Papanastasiou, K. Sapalidis, D. G. Goulis, N. Michalopoulos, E. Mareti, S. Mantalovas, and I. Kesisoglou, “Thyroid nodules as a risk factor for thyroid cancer in patients with graves’ disease: A systematic review and meta-analysis of observational studies in surgically treated patients,” Clinical Endocrinology, vol. 91, no. 4, pp. 571–577, 2019.
  63. H. Sadeghi, M. Rafei, M. Bahrami, A. Haghdoost, and Y. Shabani, “Attributable risk fraction of four lifestyle risk factors of thyroid cancer: a meta-analysis,” Journal of Public Health, vol. 40, pp. e91–e98, 08 2017.
  64. A. Matrone, F. Ferrari, F. Santini, and R. Elisei, “Obesity as a risk factor for thyroid cancer,” Current Opinion in Endocrinology, Diabetes and Obesity, vol. 27, no. 5, pp. 358–363, 2020.
  65. C. M. Kitahara and J. A. Sosa, “Understanding the ever-changing incidence of thyroid cancer,” Nature Reviews Endocrinology, vol. 16, no. 11, pp. 617–618, 2020.
  66. M. B. Zimmermann and V. Galetti, “Iodine intake as a risk factor for thyroid cancer: a comprehensive review of animal and human studies,” Thyroid research, vol. 8, no. 1, pp. 1–21, 2015.
  67. C. W. Jin and K. Jeongseon, “Dietary factors and the risk of thyroid cancer: A review,” Clinical Nutrition Research, vol. 3, no. 2, pp. 75–88, 2014.
  68. M. A. Marcello, L. L. Cunha, F. A. Batista, and L. S. Ward, “Obesity and thyroid cancer,” Endocrine-related cancer, vol. 21, no. 5, pp. T255–T271, 2014.
  69. A. Kyriacou, V. Tziaferi, and M. Toumba, “Stress, thyroid dysregulation and thyroid cancer in children and adolescents: Proposed impending mechanisms,” Hormone Research in Paediatrics, 2022.
  70. V. Zivaljevic, N. Slijepcevic, S. Sipetic, I. Paunovic, A. Diklic, G. Zoric, and N. Kalezic, “Risk factors for well-differentiated thyroid cancer in men,” Tumori Journal, vol. 99, no. 4, pp. 458–462, 2013.
  71. F. Asif, M. R. Ahmad, and A. Majid, “Risk factors for thyroid cancer in females using a logit model in lahore, pakistan,” Asian Pacific Journal of Cancer Prevention, vol. 16, no. 15, pp. 6243–6247, 2015.
  72. K. J. Stansifer, J. F. Guynan, B. M. Wachal, and R. B. Smith, “Modifiable risk factors and thyroid cancer,” Otolaryngology–Head and Neck Surgery, vol. 152, no. 3, pp. 432–437, 2015.
  73. L.-Z. Cao, X.-D. Peng, J.-P. Xie, F.-H. Yang, H.-L. Wen, and S. Li, “The relationship between iodine intake and the risk of thyroid cancer: a meta-analysis,” Medicine, vol. 96, no. 20, 2017.
  74. C.-H. Tseng, “Thyroid cancer risk is not increased in diabetic patients,” PLOS ONE, vol. 7, no. 12, 2012.
  75. C. L. Meinhold, E. Ron, S. J. Schonfeld, B. H. Alexander, D. M. Freedman, M. S. Linet, and A. Berrington de González, “Nonradiation risk factors for thyroid cancer in the us radiologic technologists study,” American Journal of Epidemiology, vol. 171, pp. 242–252, 11 2009.
  76. Y. Lun, X. Wu, Q. Xia, Y. Han, X. Zhang, Z. Liu, F. Wang, Z. Duan, S. Xin, and J. Zhang, “Hashimoto’s thyroiditis as a risk factor of papillary thyroid cancer may improve cancer prognosis,” Otolaryngology–Head and Neck Surgery, vol. 148, no. 3, pp. 396–402, 2013.
  77. C. Xhaard, Y. Ren, E. Clero, S. Maillard, P. Brindel, F. Rachedi, F. Bost-Bezeaud, F. Doyon, C. Rubino, and F. de Vathaire, “Differentiated thyroid carcinoma risk factors in french polynesia,” Asian Pacific journal of cancer prevention, vol. 15, no. 6, pp. 2675–2680, 2014.
  78. Y. Cao, Z. Wang, J. Gu, F. Hu, Y. Qi, Q. Yin, Q. Sun, G. Li, and B. Quan, “Reproductive factors but not hormonal factors associated with thyroid cancer risk: a systematic review and meta-analysis,” BioMed Research International, vol. 2015, 2015.
  79. C. Xhaard, F. de Vathaire, E. Cléro, S. Maillard, Y. Ren, F. Borson-Chazot, G. Sassolas, C. Schvartz, M. Colonna, B. Lacour, A. Danzon, M. Velten, E. Marrer, L. Bailly, E. M. Barjoan, M. Schlumberger, J. Orgiazzi, E. Adjadj, and C. Rubino, “Anthropometric risk factors for differentiated thyroid cancer in young men and women from eastern france: A case-control study,” American Journal of Epidemiology, vol. 182, pp. 202–214, 06 2015.
  80. P. Wang, L. Lv, F. Qi, and F. Qiu, “Increased risk of papillary thyroid cancer related to hormonal factors in women,” Tumor Biology, vol. 36, no. 7, pp. 5127–5132, 2015.
  81. J. Luo, M. Hendryx, J. E. Manson, X. Liang, and K. L. Margolis, “Hysterectomy, oophorectomy, and risk of thyroid cancer,” The Journal of Clinical Endocrinology & Metabolism, vol. 101, pp. 3812–3819, 07 2016.
  82. F. Khodamoradi, M. Ghoncheh, A. Mehri, S. Hassanipour, and H. Salehiniya, “Incidence, mortality, and risk factors of thyroid cancer in the world: a review,” World Cancer Research Journal, vol. 5, no. 2, 2018.
  83. K. Y., M. Gh., S. P., and G. Sh., “Assessment of thyroid cancer risk factors in kurdistan province,” Scientific Journal of Kurdistan University of Medical Sciences, vol. 23, no. 3, pp. 10–18, 2018.
  84. M. Mileva, B. Stoilovska, A. Jovanovska, A. Ugrinska, G. Petrushevska, S. Kostadinova-Kunovska, D. Miladinova, and V. Majstorov, “Thyroid cancer detection rate and associated risk factors in patients with thyroid nodules classified as bethesda category iii,” Radiology and oncology, vol. 52, no. 4, p. 370, 2018.
  85. A. S. Mannathazhathu, P. S. George, S. Sudhakaran, D. Vasudevan, J. K. KM, C. Booth, and A. Mathew, “Reproductive factors and thyroid cancer risk: Meta-analysis,” Head & neck, vol. 41, no. 12, pp. 4199–4208, 2019.
  86. Y. Nagayama, “Thyroid autoimmunity and thyroid cancer–the pathogenic connection: a 2018 update,” Hormone and Metabolic Research, vol. 50, no. 12, pp. 922–931, 2018.
  87. J.-H. Park, M. Choi, J.-H. Kim, J. Kim, K. Han, B. Kim, D.-H. Kim, and Y.-G. Park, “Metabolic syndrome and the risk of thyroid cancer: A nationwide population-based cohort study,” Thyroid, vol. 30, no. 10, pp. 1496–1504, 2020.
  88. J. M. Han, T. Y. Kim, M. J. Jeon, J. H. Yim, W. G. Kim, D. E. Song, S. J. Hong, S. J. Bae, H.-K. Kim, M.-H. Shin, Y. K. Shong, and W. B. Kim, “Obesity is a risk factor for thyroid cancer in a large, ultrasonographically screened population,” Eur J Endocrinol, vol. 168, no. 6, pp. 879–886, 2013.
  89. S. Zhao, X. Jia, X. Fan, L. Zhao, P. Pang, Y. Wang, Y. Luo, F. Wang, G. Yang, X. Wang, W. Gu, L. Zang, Y. Pei, J. Du, J. Ba, J. Dou, Y. Mu, and Z. Lyu, “Association of obesity with the clinicopathological features of thyroid cancer in a large, operative population: a retrospective case-control study,” Medicine, vol. 98, no. 50, p. e18213, 2019.
  90. S. I. Noureldine and R. P. Tufano, “Association of hashimoto’s thyroiditis and thyroid cancer,” Current Opinion in Oncology, vol. 27, no. 1, pp. 21–25, 2015.
  91. C. M. Kitahara, D. Kormendiné Farkas, J. O. L. Jørgensen, D. Cronin-Fenton, and H. T. Sørensen, “Benign thyroid diseases and risk of thyroid cancer: A nationwide cohort study,” The Journal of Clinical Endocrinology & Metabolism, vol. 103, pp. 2216–2224, 03 2018.
  92. X. Shen, G. Zhu, R. Liu, D. Viola, R. Elisei, E. Puxeddu, L. Fugazzola, C. Colombo, B. Jarzab, A. Czarniecka, A. K. Lam, C. Mian, F. Vianello, L. Yip, G. Riesco-Eizaguirre, P. Santisteban, C. J. O’Neill, M. S. Sywak, R. Clifton-Bligh, B. Bendlova, V. Sýkorová, and M. Xingcorresponding, “Patient age–associated mortality risk is differentiated by braf v600e status in papillary thyroid cancer,” Journal of Clinical Oncology, vol. 36, no. 5, p. 438, 2018.
  93. G. Pellegriti, F. Frasca, C. Regalbuto, S. Squatrito, and R. Vigneri, “Worldwide increasing incidence of thyroid cancer: update on epidemiology and risk factors,” Journal of cancer epidemiology, 2013.
  94. B. Aschebrook-Kilfoy, M. H. Ward, C. T. D. Valle, and M. C. Friesen, “Occupation and thyroid cancer,” Occupational and environmental medicine, vol. 71, no. 5, 2014.
  95. Y. Liu, L. Su, and H. Xiao, “Review of factors related to the thyroid cancer epidemic,” International journal of endocrinology, vol. 2017, 2017.
  96. A. A. Efanov, A. V. Brenner, T. I. Bogdanova, L. M. Kelly, P. Liu, M. P. Little, A. I. Wald, M. Hatch, L. Y. Zurnadzy, M. N. Nikiforova, V. Drozdovitch, R. Leeman-Neill, K. Mabuchi, M. D. Tronko, S. J. Chanock, and Y. E. Nikiforov, “Investigation of the relationship between radiation dose and gene mutations and fusions in post-chernobyl thyroid cancer,” JNCI: Journal of the National Cancer Institute, vol. 110, pp. 371–378, 11 2017.
  97. M. A. Han and J. H. Kim, “Diagnostic x-ray exposure and thyroid cancer risk: Systematic review and meta-analysis,” Thyroid, vol. 28, no. 2, pp. 220–228, 2018.
  98. T. Pappa and M. Alevizaki, “Obesity and thyroid cancer: A clinical update,” Thyroid, vol. 24, no. 2, pp. 190–199, 2014.
  99. H. Kwon, Y. Chang, A. Cho, J. Ahn, S. E. Park, C.-Y. Park, W.-Y. Lee, K.-W. Oh, S.-W. Park, H. Shin, S. Ryu, and E.-J. Rhee, “Metabolic obesity phenotypes and thyroid cancer risk: A cohort study,” Thyroid, vol. 29, no. 3, pp. 349–358, 2019.
  100. S.-Y. An, S. Y. Kim, D. J. Oh, C. Min, S. Sim, and H. G. Choi, “Obesity is positively related and tobacco smoking and alcohol consumption are negatively related to an increased risk of thyroid cancer,” Scientific reports, vol. 10, no. 1, pp. 1–9, 2020.
  101. J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation,” SIGMOD Rec., vol. 29, p. 1–12, may 2000.
  102. P. Umasankar and V. Thiagarasu, “Decision support system for heart disease diagnosis using interval vague set and fuzzy association rule mining,” in 2018 4th International Conference on Devices, Circuits and Systems (ICDCS), pp. 223–227, 2018.
  103. F. Tao, F. Murtagh, and M. Farid, “Weighted association rule mining using weighted support and significance framework,” in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (New York, NY, USA), p. 661–666, Association for Computing Machinery, 2003.
  104. K. S. Lakshmi and G. Vadivu, “A novel approach for disease comorbidity prediction using weighted association rule mining,” Journal of Ambient Intelligence and Humanized Computing, vol. 9, pp. 1–8, 2019.
  105. A. Yavari, A. Rajabzadeh, and F. Abdali-Mohammadi, “Profile-based assessment of diseases affective factors using fuzzy association rule mining approach: A case study in heart diseases,” Journal of Biomedical Informatics, vol. 116, p. 103695, 2021.
  106. V. Anand and D. Koundal, “Computer-assisted diagnosis of thyroid cancer using medical images: A survey,” in Proceedings of ICRIC 2019, pp. 543–559, Springer, 2020.
  107. W.-K. Chan, J.-H. Sun, M.-J. Liou, Y.-R. Li, W.-Y. Chou, F.-H. Liu, S.-T. Chen, and S.-J. Peng, “Using deep convolutional neural networks for enhanced ultrasonographic image diagnosis of differentiated thyroid cancer,” Biomedicines, vol. 9, no. 12, 2021.
  108. J. Ma, F. Wu, T. Jiang, Q. Zhao, and D. Kong, “Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks,” International journal of computer assisted radiology and surgery, vol. 12, no. 11, pp. 1895–1910, 2017.
  109. B. Chouiha and A. Amamra, “Thyroid nodules recognition in ultrasound images based on imagenet top-performing deep convolutional neural networks,” in International Conference on Computing Systems and Applications, pp. 313–322, Springer, 2020.
  110. Z. Zhao, C. Yang, Q. Wang, H. Zhang, L. Shi, and Z. Zhang, “A deep learning-based method for detecting and classifying the ultrasound images of suspicious thyroid nodules,” Medical Physics, vol. 48, no. 12, pp. 7959–7970, 2021.
  111. O. A. Adebisi, J. A. Ojo, and T. O. Bello, “Computer aided diagnosis system for classification of abnormalities in thyroid nodules ultrasound images using deep learning,” Journal of Computer Engineering, vol. 22, pp. 60–66, 2020.
  112. X. Shen, X. Ouyang, T. Liu, and D. Shen, “Cascaded networks for thyroid nodule diagnosis from ultrasound images,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 145–154, Springer, 2020.
  113. H. A. Nugroho, Zulfanahri, E. L. Frannita, I. Ardiyanto, and L. Choridah, “Computer aided diagnosis for thyroid cancer system based on internal and external characteristics,” Journal of King Saud University - Computer and Information Sciences, vol. 33, no. 3, pp. 329–339, 2021.
  114. V. V. Vadhiraj, A. Simpkin, J. O’Connell, N. Singh Ospina, S. Maraka, and D. T. O’Keeffe, “Ultrasound image classification of thyroid nodules using machine learning techniques,” Medicina, vol. 57, no. 6, 2021.
  115. J. Chi, E. Walia, P. Babyn, J. Wang, G. Groot, and M. Eramian, “Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network,” Journal of digital imaging, vol. 30, no. 4, pp. 477–486, 2017.
  116. N. S. Narayan, P. Marziliano, and C. G. Hobbs, “Automatic removal of manually induced artefacts in ultrasound images of thyroid gland,” in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3399–3402, IEEE, 2013.
  117. H. Mugasa, S. Dua, J. E. Koh, Y. Hagiwara, O. S. Lih, C. Madla, P. Kongmebhol, K. H. Ng, and U. R. Acharya, “An adaptive feature extraction model for classification of thyroid lesions in ultrasound images,” Pattern Recognition Letters, vol. 131, pp. 463–473, 2020.
  118. H. A. Nugroho, E. L. Frannita, and A. H. T. Hutami, “Thyroid nodules categorization based on margin features using deep learning,” in 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pp. 499–504, 2020.
  119. X. Zhang, V. C. Lee, J. Rong, J. C. Lee, and F. Liu, “Deep convolutional neural networks in thyroid disease detection: A multi-classification comparison by ultrasonography and computed tomography,” Computer Methods and Programs in Biomedicine, vol. 220, p. 106823, 2022.
  120. M. Buda, B. Wildman-Tobriner, K. Castor, J. K. Hoang, and M. A. Mazurowski, “Deep learning-based segmentation of nodules in thyroid ultrasound: Improving performance by utilizing markers present in the images,” Ultrasound in Medicine & Biology, vol. 46, no. 2, pp. 415–421, 2020.
  121. S. Ghosh, N. Das, I. Das, and U. Maulik, “Understanding deep learning techniques for image segmentation,” ACM Comput. Surv., vol. 52, no. 4, 2019.
  122. P. Poudel, A. Illanes, D. Sheet, and M. Friebe, “Evaluation of commonly used algorithms for thyroid ultrasound images segmentation and improvement using machine learning approaches,” Journal of healthcare engineering, vol. 2018, 2018.
  123. C. Rother, V. Kolmogorov, and A. Blake, “"grabcut": Interactive foreground extraction using iterated graph cuts,” ACM Trans. Graph., vol. 23, p. 309–314, aug 2004.
  124. O’Reilly Media, 2019.
  125. W. Rawat and Z. Wang, “Deep convolutional neural networks for image classification: A comprehensive review,” Neural Computation, vol. 29, pp. 2352–2449, 09 2017.
  126. Nature, 2015.
  127. W. Shang, K. Sohn, D. Almeida, and H. Lee, “Understanding and improving convolutional neural networks via concatenated rectified linear units,” in International Conference on Machine Learning, pp. 2217–2225, PMLR, 2016.
  128. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.
  129. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” In: Advances in Neural Information Processing Systems, pp. 1097–1105, 2012.
  130. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 2014.
  131. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
  132. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in IEEE conference on computer vision and pattern recognition, pp. 1–9, IEEE, 2015.
  133. O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, pp. 234–241, Springer, 2015.
  134. S. Zhou, H. Wu, J. Gong, T. Le, H. Wu, Q. Chen, and Z. Xu, “Mark-guided segmentation of ultrasonic thyroid nodules using deep learning,” in Proceedings of the 2nd International Symposium on Image Computing and Digital Medicine, ISICDM 2018, p. 21–26, Association for Computing Machinery, 2018.
  135. S. Stenman, D. Bychkov, H. Kücükel, N. Linder, C. Haglund, J. Arola, and J. Lundin, “Antibody supervised training of a deep learning based algorithm for leukocyte segmentation in papillary thyroid carcinoma,” IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 2, pp. 422–428, 2021.
  136. R. Daulatabad, R. Vega, J. L. Jaremko, J. Kapur, A. R. Hareendranathan, and K. Punithakumar, “Integrating user-input into deep convolutional neural networks for thyroid nodule segmentation,” in 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 2637–2640, 2021.
  137. J. Ding, Z. Huang, M. Shi, and C. Ning, “Automatic thyroid ultrasound image segmentation based on u-shaped network,” in 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1–5, 2019.
  138. X. He, B. J. Guo, Y. Lei, Y. Liu, T. Wang, W. J. Curran, L. J. Zhang, T. Liu, and X. Yang, “3d thyroid segmentation in ct using self-attention convolutional neural network,” in Medical Imaging 2020: Computer-Aided Diagnosis, vol. 11314, p. 1131445, International Society for Optics and Photonics, 2020.
  139. H. A. Nugroho, E. L. Frannita, and R. Nurfauzi, “An automated detection and segmentation of thyroid nodules using res-unet,” in 2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), pp. 181–185, 2021.
  140. K. Cao and X. Zhang, “An improved res-unet model for tree species classification using airborne high-resolution images,” Remote Sensing, vol. 12, no. 7, 2020.
  141. Q. Yang, C. Geng, R. Chen, C. Pang, R. Han, L. Lyu, and Y. Zhang, “Dmu-net: Dual-route mirroring u-net with mutual learning for malignant thyroid nodule segmentation,” Biomedical Signal Processing and Control, vol. 77, p. 103805, 2022.
  142. Y. Zhang, H. Lai, and W. Yang, “Cascade unet and ch-unet for thyroid nodule segmentation and benign and malignant classification,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 129–134, Springer, 2020.
  143. A. Shahroudnejad, R. Vega, A. Forouzandeh, S. Balachandran, J. Jaremko, M. Noga, A. R. Hareendranathan, J. Kapur, and K. Punithakumar, “Thyroid nodule segmentation and classification using deep convolutional neural network and rule-based classifiers,” in 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 3118–3121, 2021.
  144. V. Kumar, J. Webb, A. Gregory, D. D. Meixner, J. M. Knudsen, M. Callstrom, M. Fatemi, and A. Alizad, “Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning,” IEEE Access, vol. 8, pp. 63482–63496, 2020.
  145. F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” in International Conference on Learning Representations (ICLR), May 2016.
  146. X. Li, S. Wang, X. Wei, J. Zhu, R. Yu, M. Zhao, M. Yu, Z. Liu, and S. Liu, “Fully convolutional networks for ultrasound image segmentation of thyroid nodules,” in 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 886–890, 2018.
  147. H. Gong, G. Chen, R. Wang, X. Xie, M. M. abd Yizhou Yu, F. Chen, and G. Li, “Multi-task learning for thyroid nodule segmentation with thyroid region prior,” in IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 257–261, IEEE, 2021.
  148. C. Zhou, J. Geng, and H. Li, “Segmentation of ultrasound thyroid nodules based on encoder-decoder structure,” in 2021 International Conference on Internet, Education and Information Technology (IEIT), pp. 333–337, 2021.
  149. E. J. Gomes Ataide, N. Ponugoti, A. Illanes, S. Schenke, M. Kreissl, and M. Friebe, “Thyroid nodule classification for physician decision support using machine learning-evaluated geometric and morphological features,” Sensors, vol. 20, no. 21, 2020.
  150. X. Yang, S. Qiu, and Q. Luo, “Feature-based discrimination of thyroid cancer on ultrasound images,” in 2020 IEEE 3rd International Conference on Electronics Technology (ICET), pp. 834–839, 2020.
  151. F. Li, D. Pan, Y. He, Y. Wu, J. Peng, J. Li, Y. Wang, H. Yang, and J. Chen, “Using ultrasound features and radiomics analysis to predict lymph node metastasis in patients with thyroid cancer,” BMC Surg, vol. 20, no. 315, 2020.
  152. X. M. Keutgen, H. Li, K. Memeh, J. C. Busch, J. Williams, L. Lan, D. Sarne, B. Finnerty, P. Angelos, T. J. Fahey, and M. L. Giger, “A machine-learning algorithm for distinguishing malignant from benign indeterminate thyroid nodules using ultrasound radiomic features,” Journal of Medical Imaging, vol. 9, no. 3, p. 034501, 2022.
  153. P. Lambin, E. Rios-Velazquez, R. Leijenaar, S. Carvalho, R. G. van Stiphout, P. Granton, C. M. Zegers, R. Gillies, R. Boellard, A. Dekker, and H. J. Aerts, “Radiomics: Extracting more information from medical images using advanced feature analysis,” European Journal of Cancer, vol. 48, no. 4, pp. 441–446, 2012.
  154. R. Wei, H. Wang, L. Wang, W. Hu, X. Sun, Z. Dai, J. Zhu, H. Li, Y. Ge, and B. Song, “Radiomics based on multiparametric mri for extrathyroidal extension feature prediction in papillary thyroid cancer,” BMC Med Imaging, vol. 21, no. 20, 2021.
  155. W. Hu, H. Wang, R. Wei, L. Wang, Z. Dai, S. Duan, Y. Ge, P.-Y. Wu, and B. Song, “Mri-based radiomics analysis to predict preoperative lymph node metastasis in papillary thyroid carcinoma,” Gland Surgery, vol. 9, no. 5, p. 1214, 2020.
  156. W. Lu, L. Zhong, D. Dong, M. Fang, Q. Dai, S. Leng, L. Zhang, W. Sun, J. Tian, J. Zheng, and Y. Jin, “Radiomic analysis for preoperative prediction of cervical lymph node metastasis in patients with papillary thyroid carcinoma,” European Journal of Radiology, vol. 118, pp. 231–238, 2019.
  157. Y. Zhou, G.-Y. Su, H. Hu, Y.-Q. Ge, Y. Si, M.-P. Shen, X.-Q. Xu, and F.-Y. Wu, “Radiomics analysis of dual-energy ct-derived iodine maps for diagnosing metastatic cervical lymph nodes in patients with papillary thyroid cancer,” European radiology, vol. 30, no. 11, pp. 6251–6262, 2020.
  158. Y.-y. Wu, C. Wei, C.-b. Wang, N.-y. Li, P. Zhang, and J.-n. Dong, “Preoperative prediction of cervical nodal metastasis in papillary thyroid carcinoma: Value of quantitative dual-energy ct parameters and qualitative morphologic features,” American Journal of Roentgenology, vol. 216, no. 5, pp. 1335–1343, 2021.
  159. T. Liu, S. Xie, Y. Zhang, J. Yu, L. Niu, and W. Sun, “Feature selection and thyroid nodule classification using transfer learning,” in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 1096–1099, 2017.
  160. J. Tang, S. Alelyani, and H. Liu, “Feature selection for classification: A review,” in Data classification: Algorithms and applications, CRC Press, 2014.
  161. T. Jaakkola and D. Haussler, “Exploiting generative models in discriminative classifiers,” in Advances in Neural Information Processing Systems (M. Kearns, S. Solla, and D. Cohn, eds.), vol. 11, MIT Press, 1998.
  162. L. A. R. Kenji Kira, “The feature selection problem: Traditional methods and a new algorithm,” in AAAI-92 Proceedings, vol. 2, pp. 129–134, 1992.
  163. M. Yamada, W. Jitkrittum, L. Sigal, E. P. Xing, and M. Sugiyama, “High-dimensional feature selection by feature-wise non-linear lasso,” Neural Computation, vol. 26, no. 1, pp. 185–207, 2014.
  164. K. Pearson, “Liii. on lines and planes of closest fit to systems of points in space,” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, vol. 2, no. 11, pp. 559–572, 1901.
  165. H. Abdi and L. J. Williams, “Principal component analysis,” Wiley interdisciplinary reviews: computational statistics, vol. 2, no. 4, pp. 433–459, 2010.
  166. L.-N. Li, J.-H. Ouyang, H.-L. Chen, and D.-Y. Liu, “A computer aided diagnosis system for thyroid disease using extreme learning machine,” Journal of medical systems, vol. 36, no. 5, pp. 3327–3337, 2012.
  167. Q. Pan, Y. Zhang, M. Zuo, L. Xiang, and D. Chen, “Improved ensemble classification method of thyroid disease based on random forest,” in 2016 8th International Conference on Information Technology in Medicine and Education (ITME), pp. 567–571, 2016.
  168. K. J. Lim, C. S. Choi, D. Y. Yoon, S. K. Chang, K. K. Kim, H. Han, S. S. Kim, J. Lee, and Y. H. Jeon, “Computer-aided diagnosis for the differentiation of malignant from benign thyroid nodules on ultrasonography,” Academic Radiology, vol. 15, no. 7, pp. 853–858, 2008.
  169. Y. I. Liu, A. Kamaya, T. S. Desser, and D. L. Rubin, “A bayesian classifier for differentiating benign versus malignant thyroid nodules using sonographic features,” in AMIA Annual Symposium Proceedings, vol. 2008, p. 419, 2008.
  170. C.-Y. Chang, S.-J. Chen, and M.-F. Tsai, “Application of support-vector-machine-based method for feature selection and classification of thyroid nodules in ultrasound images,” Pattern Recognition, vol. 43, no. 10, pp. 3494–3506, 2010.
  171. L.-C. Zhu, Y.-L. Ye, W.-H. Luo, M. Su, H.-P. Wei, X.-B. Zhang, J. Wei, and C.-L. Zou, “A model to discriminate malignant from benign thyroid nodules using artificial neural network,” PLoS One, vol. 8, no. 12, p. e82211, 2013.
  172. E. Kim, M. Corte-Real, and Z. Baloch, “A deep semantic mobile application for thyroid cytopathology,” Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations, vol. 9789, p. 97890A, 2016.
  173. Y. Chang, A. K. Paul, N. Kim, J. H. Baek, Y. J. Choi, E. J. Ha, K. D. Lee, H. S. Lee, D. Shin, and N. Kim, “Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: a comparison with radiologist-based assessments,” Medical physics, vol. 43, no. 1, pp. 554–567, 2016.
  174. H. Wu, Z. Deng, B. Zhang, Q. Liu, and J. Chen, “Classifier model based on machine learning algorithms: application to differential diagnosis of suspicious thyroid nodules via sonography,” American Journal of Roentgenology, vol. 207, no. 4, pp. 859–864, 2016.
  175. T. Liu, S. Xie, J. Yu, L. Niu, and W. Sun, “Classification of thyroid nodules in ultrasound images using deep model based transfer learning and hybrid features,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 919–923, 2017.
  176. S. Y. Ko, J. H. Lee, J. H. Yoon, H. Na, E. Hong, K. Han, I. Jung, E.-K. Kim, H. J. Moon, V. Y. Park, E. Lee, and J. Y. Kwak, “Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound,” Head & neck, vol. 41, no. 4, pp. 885–891, 2019.
  177. T. Liu, Q. Guo, C. Lian, X. Ren, S. Liang, J. Yu, L. Niu, W. Sun, and D. Shen, “Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks,” Medical Image Analysis, vol. 58, p. 101555, 2019.
  178. X. Liang, J. Yu, J. Liao, and Z. Chen, “Convolutional neural network for breast and thyroid nodules diagnosis in ultrasound imaging,” BioMed Research International, 2020.
  179. H. Wang, B. Song, N. Ye, J. Ren, X. Sun, Z. Dai, Y. Zhang, and B. T. Chen, “Machine learning-based multiparametric mri radiomics for predicting the aggressiveness of papillary thyroid carcinoma,” European Journal of Radiology, vol. 122, p. 108755, 2020.
  180. S. Gitto, G. Grassi, C. D. Angelis, C. G. Monaco, S. Sdao, F. Sardanelli, L. M. Sconfienza, and G. Mauri, “A computer-aided diagnosis system for the assessment and characterization of low-to-high suspicion thyroid nodules on ultrasound,” La radiologia medica, vol. 124, no. 2, pp. 118–125, 2019.
  181. Y. J. Yoo, E. J. Ha, Y. J. Cho, H. L. Kim, M. Han, and S. Y. Kang, “Computer-aided diagnosis of thyroid nodules via ultrasonography: initial clinical experience,” Korean journal of radiology, vol. 19, no. 4, p. 665, 2018.
  182. E. Y. Jeong, H. L. Kim, E. J. Ha, S. Y. Park, Y. J. Cho, and M. Han, “Computer-aided diagnosis system for thyroid nodules on ultrasonography: diagnostic performance and reproducibility based on the experience level of operators,” European radiology, vol. 29, no. 4, pp. 1978–1985, 2019.
  183. N. Carnes, Predicting risk of malignancy in patients with indeterminate thyroid nodules. PhD thesis, Boston University, 2018.
  184. Q. Guan, Y. Wang, J. Du, Y. Qin, H. Lu, J. Xiang, and F. Wang, “Deep learning based classification of ultrasound images for thyroid nodules: a large scale of pilot study,” Annals of translational medicine, vol. 7, no. 7, 2019.
  185. I. Shin, Y. J. Kim, K. Han, E. Lee, H. J. Kim, J. H. Shin, H. J. Moon, J. H. Youk, K. G. Kim, , and J. Y. Kwak, “Application of machine learning to ultrasound images to differentiate follicular neoplasms of the thyroid gland,” Ultrasonography, vol. 39, no. 3, p. 257, 2020.
  186. J. Xia, H. Chen, Q. Li, M. Zhou, L. Chen, Z. Cai, Y. Fang, and H. Zhou, “Ultrasound-based differentiation of malignant and benign thyroid nodules: An extreme learning machine approach,” Computer Methods and Programs in Biomedicine, vol. 147, pp. 37–49, 2017.
  187. B. Zhang, J. Tian, S. Pei, Y. Chen, X. He, Y. Dong, L. Zhang, X. Mo, W. Huang, S. Cong, and S. Zhang, “Machine learning–assisted system for thyroid nodule diagnosis,” Thyroid, vol. 29, no. 6, pp. 858–867, 2019.
  188. A. A. A. Arafa, N. El-Sokary, A. Asad, and H. Hefny, “Computer-aided detection system for breast cancer based on gmm and svm,” Arab Journal of Nuclear Sciences and Applications, vol. 52, no. 2, pp. 142–150, 2019.
  189. K. Sharma, A. Kaur, and S. Gujral, “Brain tumor detection based on machine learning algorithms,” International Journal of Computer Applications, vol. 103, no. 1, pp. 7–11, 2014.
  190. S. Naeem, A. Ali, S. Qadri, W. Khan Mashwani, N. Tairan, H. Shah, M. Fayaz, F. Jamal, C. Chesneau, and S. Anam, “Machine-learning based hybrid-feature analysis for liver cancer classification using fused (mr and ct) images,” Applied Sciences, vol. 10, no. 9, 2020.
  191. X. Zeng, H. Chen, Y. Luo, and W. Ye, “Automated diabetic retinopathy detection based on binocular siamese-like convolutional neural network,” IEEE Access, vol. 7, pp. 30744–30753, 2019.
  192. L. Wang, Z. Q. Lin, and A. Wong, “Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images,” Scientific Reports, vol. 10, no. 1, pp. 1–12, 2020.
  193. I. D. Apostolopoulos and T. A. Mpesiana, “Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks,” Physical and Engineering Sciences in Medicine, vol. 43, no. 2, pp. 635–640, 2020.
  194. Z. Liang, A. Powell, I. Ersoy, M. Poostchi, K. Silamut, K. Palaniappan, P. Guo, M. A. Hossain, A. Sameer, R. J. Maude, J. X. Huang, S. Jaeger, and G. Thoma, “Cnn-based image analysis for malaria diagnosis,” in 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 493–496, 2016.
  195. Y. I. Liu, A. Kamaya, T. S. Desser, and D. L. Rubin, “A bayesian classifier for differentiating benign versus malignant thyroid nodules using sonographic features,” in AMIA Annual Symposium Proceedings, p. 419, American Medical Informatics Association, 2008.
  196. N. Singh and A. Jindal, “A segmentation method and comparison of classification methods for thyroid ultrasound images,” International Journal of Computer Applications, vol. 50, no. 11, pp. 43–49, 2012.
  197. X. Li, S. Zhang, Q. Zhang, X. Wei, Y. Pan, J. Zhao, X. Xin, C. Qin, X. Wang, J. Li, F. Yang, Y. Zhao, M. Yang, Q. Wang, Z. Zheng, X. Zheng, X. Yang, C. T. Whitlow, M. N. Gurcan, L. Zhang, X. Wang, B. C. Pasche, M. Gao, W. Zhang, and K. Chen, “Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study,” The Lancet Oncology, vol. 20, no. 2, pp. 193–201, 2019.
  198. M. Buda, B. Wildman-Tobriner, J. K. Hoang, D. Thayer, F. N. Tessler, W. D. Middleton, and M. A. Mazurowski, “Management of thyroid nodules seen on us images: deep learning may match performance of radiologists,” Radiology, vol. 292, no. 3, pp. 695 – 701, 2019.
  199. Y.-C. Zhu, A. AlZoubi, S. Jassim, Q. Jiang, Y. Zhang, Y.-B. Wang, X.-D. Ye, and H. DU, “A generic deep learning framework to classify thyroid and breast lesions in ultrasound images,” Ultrasonics, vol. 110, p. 106300, 2021.
  200. H. A. Nugroho and E. L. Frannita, “Impact of implementing data balancing method in intelligent thyroid cancer detection,” in 2021 International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE), pp. 102–106, 2021.
  201. J. Ma, F. Wu, J. Zhu, D. Xu, and D. Kong, “A pre-trained convolutional neural network based method for thyroid nodule diagnosis,” in Ultrasonics, pp. 221–230, Elsevier, 2017.
  202. Y. Wang, Q. Guan, I. Lao, L. Wang, Y. Wu, D. Li, Q. Ji, Y. Wang, Y. Zhu, H. Lu, and J. Xiang, “Using deep convolutional neural networks for multi-classification of thyroid tumor by histopathology: a large-scale pilot study,” Annals of translational medicine, vol. 7, no. 18, 2019.
  203. D. Dov, S. Z. Kovalsky, J. Cohen, D. E. Range, R. Henao, and L. Carin, “Thyroid cancer malignancy prediction from whole slide cytopathology images,” in Machine Learning for Healthcare Conference, pp. 553–570, PMLR, 2019.
  204. V. G. Buddhavarapu and A. A. J. J, “An experimental study on classification of thyroid histopathology images using transfer learning,” Pattern Recognition Letters, vol. 140, pp. 1–9, 2020.
  205. J. A. Chandio, G. A. Mallah, and N. A. Shaikh, “Decision support system for classification medullary thyroid cancer,” IEEE Access, vol. 8, pp. 145216–145226, 2020.
  206. G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017.
  207. J. H. Lee, E. J. Ha, and J. H. Kim, “Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with ct,” European radiology, vol. 29, no. 10, pp. 5452–5457, 2019.
  208. X. Zhang, V. C. S. Lee, J. Rong, F. Liu, and H. Kong, “Multi-channel convolutional neural network architectures for thyroid cancer detection,” PLOS ONE, vol. 17, pp. 1–26, 01 2022.
  209. R. Zhang, Q. Liu, H. Cui, X. Wang, S. Song, G. Huang, and D. Feng, “Thyroid classification via new multi-channel feature association and learning from multi-modality mri images,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 277–280, 2018.
  210. A. Naglah, F. Khalifa, R. Khaled, A. A. k. A. Razek, and A. El-Baz, “Thyroid cancer computer-aided diagnosis system using mri-based multi-input cnn model,” in 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1691–1694, 2021.
  211. H. Zhao, C. Liu, J. Ye, L. Chang, Q. Xu, B. Shi, L. Liu, Y. Yin, and B. Shi, “A comparison between deep learning convolutional neural networks and radiologists in the differentiation of benign and malignant thyroid nodules on ct images,” Endokrynologia Polska, vol. 72, no. 3, pp. 217 – 225, 2021.
  212. M. Halicek, G. Lu, J. V. Little, X. Wang, M. Patel, C. C. Griffith, M. W. El-Deiry, A. Y. Chen, and B. Fei, “Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging,” Journal of biomedical optics, vol. 22, no. 6, p. 060503, 2017.
  213. L. Ma, C. Ma, Y. Liu, and X. Wang, “Thyroid diagnosis from spect images using convolutional neural network with optimization,” Computational intelligence and neuroscience, 2019.
  214. J.-Z. Cheng, D. Ni, Y.-H. Chou, J. Qin, C.-M. Tiu, Y.-C. Chang, C.-S. Huang, D. Shen, and C.-M. Chen, “Computer-aided diagnosis with deep learning architecture: applications to breast lesions in us images and pulmonary nodules in ct scans,” Scientific reports, vol. 6, pp. 1–13, 2016.
  215. V. Alex, K. Vaidhya, S. Thirunavukkarasu, C. Kesavadas, and G. Krishnamurthi, “Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation,” Journal of Medical Imaging, vol. 41, p. 041311, 2017.
  216. R. Hodson, Precision medicine. Nature, 2016.
  217. A. Schäffler, “Hormone replacement after thyroid and parathyroid surgery,” Deutsches Ärzteblatt International, vol. 107, no. 47, 2011.
  218. J. Chen, K. Li, H. Rong, K. Bilal, N. Yang, and K. Li, “A disease diagnosis and treatment recommendation system based on big data mining and cloud computing,” Information Sciences, vol. 435, pp. 124–149, 2018.
  219. J. L. Katzman, U. Shaham, A. Cloninger, J. Bates, T. Jiang, and Y. Kluger, “Deepsurv: personalized treatment recommender system using a cox proportional hazards deep neural network,” BMC medical research methodology, vol. 18, no. 1, pp. 1–12, 2018.
  220. E. Papageorgiou, C. Stylios, and P. Groumpos, “A combined fuzzy cognitive map and decision trees model for medical decision making,” in 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6117–6120, 2006.
  221. E. Papageorgiou, C. Stylios, and P. Groumpos, “An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 12, pp. 1326–1339, 2003.
  222. C. D. Stylios, V. C. Georgopoulos, G. A. Malandraki, and S. Chouliara, “Fuzzy cognitive map architectures for medical decision support systems,” Applied Soft Computing, vol. 8, no. 3, pp. 1243–1251, 2008. Forging the Frontiers - - Soft Computing.
  223. J. A. Cruz and D. S. Wishart, “Applications of machine learning in cancer prediction and prognosis,” Cancer informatics, vol. 2, 2006.
  224. M. Jajroudi, T. Baniasadi, L. Kamkar, F. Arbabi, M. Sanei, and M. Ahmadzade, “Prediction of survival in thyroid cancer using data mining technique,” Technology in Cancer Research & Treatment, vol. 13, no. 4, pp. 353–359, 2014.
  225. Boston, MA: Springer US, 1997.
  226. Y. M. Park and B.-J. Lee, “Machine learning-based prediction model using clinico-pathologic factors for papillary thyroid carcinoma recurrence,” Scientific Reports, vol. 11, no. 1, pp. 1–7, 2021.
  227. C. Q. Yang, L. Gardiner, H. Wang, M. T. Hueman, and D. Chen, “Creating prognostic systems for well-differentiated thyroid cancer using machine learning,” Frontiers in Endocrinology, vol. 10, p. 288, 2019.
  228. D. F. Schneider, H. Mazeh, H. Chen, and R. S. Sippel, “Lymph node ratio predicts recurrence in papillary thyroid cancer,” The oncologist, vol. 18, no. 2, p. 157, 2013.
  229. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “Imagenet large scale visual recognition challenge,” International journal of computer vision, vol. 115, no. 3, pp. 211–252, 2015.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now